Graphical Normalizing Flows

Offical codes and experiments for the paper:

Graphical Normalizing Flows, Antoine Wehenkel and Gilles Louppe. (May 2020). [arxiv]

Dependencies

The list of dependencies can be found in requirements.txt text file and installed with the following command:

pip install -r requirements.txt

Code architecture

This repository provides some code to build diverse types normalizing flow models in PyTorch. The core components are located in the models folder. The different flow models are described in the file NormalizingFlow.py and they all follow the structure of the parent class NormalizingFlow. A flow step is usually designed as a combination of a normalizer (such as the ones described in Normalizers sub-folder) with a conditioner (such as the ones described in Conditioners sub-folder). Following the code hierarchy provided makes the implementation of new conditioners, normalizers or even complete flow architecture very easy.

Paper's experiments

UCI Datasets

You first have to download the datasets with the following command:

python UCIdatasets/download_dataset.py 

Then you can run the experiment of your choice with the following command:

python UCIExperiments.py -load_config <exp-name>

where defines the experimental configuration loaded from UCIExperimentsConfigurations.yml file, e.g. power-mono-DAG. See also UCIExperiments.py for other optional arguments.

MNIST

Affine Normalizers

Graphical Conditioner

python ImageExperiments.py -dataset MNIST -b_size 100 -normalizer Affine -conditioner DAG -nb_flow 1 -nb_steps_dual 10 -l1 0. -prior_A_kernel 2

Autoregressive Conditioner

python ImageExperiments.py -dataset MNIST -b_size 100 -normalizer Affine -conditioner Autoregressive -nb_flow 1 -emb_net 1024 1024 1024 2

Coupling Conditioner

python ImageExperiments.py -dataset MNIST -b_size 100 -normalizer Affine -conditioner Coupling -nb_flow 1 -emb_net 1024 1024 1024 2

Monotonic Normalizers

Graphical Conditioner

python ImageExperiments.py -dataset MNIST -b_size 100 -normalizer Monotonic -conditioner DAG -nb_flow 1 -nb_steps_dual 10 -l1 0. -prior_A_kernel 2

Autoregressive Conditioner

python ImageExperiments.py -dataset MNIST -b_size 100 -normalizer Monotonic -conditioner Autoregressive -nb_flow 1 -emb_net 1024 1024 1024 30

Coupling Conditioner

python ImageExperiments.py -dataset MNIST -b_size 100 -normalizer Monotonic -conditioner Coupling -nb_flow 1 -emb_net 1024 1024 1024 30